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psycholing-predictors

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psycholinguistics
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surprisal
entropy
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Per-word reading predictors

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tar filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ pretty_name: Per-word reading predictors
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+ tags:
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+ - psycholinguistics
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+ - reading-times
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+ - surprisal
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+ - entropy
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+ configs:
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+ - config_name: brothers_kuperberg
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+ data_files: brothers_kuperberg.parquet
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+ - config_name: brown_spr
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+ data_files: brown_spr.parquet
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+ - config_name: bsc
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+ data_files: bsc.parquet
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+ - config_name: celer
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+ data_files: celer.parquet
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+ - config_name: copco
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+ data_files: copco.parquet
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+ - config_name: devarda2023
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+ data_files: devarda2023.parquet
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+ - config_name: dundee
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+ data_files: dundee.parquet
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+ - config_name: emtec
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+ data_files: emtec.parquet
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+ - config_name: geco
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+ data_files: geco.parquet
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+ - config_name: meco_char_de
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+ data_files: meco_char_de.parquet
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+ - config_name: meco_char_du
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+ data_files: meco_char_du.parquet
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+ - config_name: meco_char_ee
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+ data_files: meco_char_ee.parquet
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+ data_files: meco_char_en.parquet
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+ data_files: meco_char_fi.parquet
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+ - config_name: meco_char_he
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+ data_files: meco_char_he.parquet
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+ - config_name: meco_char_it
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+ data_files: meco_char_it.parquet
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+ - config_name: meco_char_ko
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+ data_files: meco_char_ko.parquet
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+ - config_name: meco_char_no
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+ data_files: meco_char_no.parquet
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+ - config_name: meco_char_ru
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+ data_files: meco_char_ru.parquet
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+ - config_name: meco_char_sp
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+ data_files: meco_char_sp.parquet
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+ - config_name: meco_char_tr
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+ data_files: meco_char_tr.parquet
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+ - config_name: meco_fi
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+ data_files: meco_fi.parquet
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+ - config_name: meco_gr
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+ - config_name: meco_he
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+ data_files: meco_he.parquet
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+ - config_name: meco_it
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+ data_files: meco_it.parquet
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+ - config_name: meco_ko
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+ data_files: meco_ko.parquet
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+ - config_name: meco_no
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+ data_files: meco_no.parquet
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+ - config_name: meco_ru
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+ data_files: meco_ru.parquet
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+ - config_name: meco_sp
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+ data_files: meco_sp.parquet
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+ - config_name: meco_tr
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+ data_files: meco_tr.parquet
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+ - config_name: natural_stories
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+ data_files: natural_stories.parquet
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+ - config_name: onestop
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+ data_files: onestop.parquet
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+ - config_name: potec
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+ data_files: potec.parquet
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+ - config_name: provo
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+ data_files: provo.parquet
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+ - config_name: sbsat
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+ data_files: sbsat.parquet
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+ - config_name: ucl_et
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+ data_files: ucl_et.parquet
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+ - config_name: ucl_spr
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+ data_files: ucl_spr.parquet
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+ ---
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+ # Per-word predictors for reading-time modeling
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+
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+ A collection of precomputed per-word predictors for computational models of
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+ human sentence processing: surprisal, entropy (Shannon and Renyi), unigram
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+ surprisal, and related quantities. One Parquet file per reading corpus.
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+
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+ ## Schema
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+
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+ | column | type | meaning |
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+ |---|---|---|
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+ | `dataset` | string | corpus name (also the file name) |
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+ | `tag` | string | cell id (model + method + config), e.g. `gpt2_standard_provo` |
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+ | `predictor` | string | predictor key, e.g. `log_p_observed`, `token_entropy`, `mc_word_entropy` |
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+ | `stimulus_id` | string | stimulus id within the dataset |
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+ | `unit_index` | int32 | 0-based word index within the stimulus |
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+ | `value` | double | predictor value (NaN where undefined) |
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+
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+ ## Loading
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+
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+ ```python
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+ import pandas as pd
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+ provo = pd.read_parquet("hf://datasets/samuki-hf/psycholing-predictors/provo.parquet")
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+ cell = provo[provo.tag == "gpt2_standard_provo"]
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+ ```
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+
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+ ## Directory layout
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+
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+ | path | contents |
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+ |---|---|
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+ | `<dataset>.parquet` | scalar per-word predictors, one file per corpus (schema above) |
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+ | `next_log_probs/` | full per-word next-token log-prob distributions (float32) |
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+ | `mcword_samples/` | Monte-Carlo sample pools behind `mc_word_entropy` |
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+ | `cont_entropy_samples/` | Monte-Carlo sample pools behind `continuation_entropy` |
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+ | `fwd_word_lookahead_samples/` | word-level look-ahead entropy samples (joint / conditional / marginal at depths k=1..K) |
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+ | `owt/unigram_counts.tsv` | OpenWebText unigram count table (count-MLE unigram-surprisal baseline) |
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+ | `modelblocks_kenlm_models/` | order-1 KenLM ARPA trained on OpenWebText |
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+ | `kenlm_o1/` | per-word OpenWebText unigram surprisal, one pickle per dataset |
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+
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+ Files under the sample and distribution directories hold `list<float>` arrays and
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+ are not dataset-viewer configs; load them by path with `pandas.read_parquet`.
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